Overview

Dataset statistics

Number of variables15
Number of observations14576
Missing cells40218
Missing cells (%)18.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 MiB
Average record size in memory120.0 B

Variable types

Categorical6
Numeric9

Alerts

Name has a high cardinality: 10327 distinct values High cardinality
Publisher has a high cardinality: 552 distinct values High cardinality
Developer has a high cardinality: 1577 distinct values High cardinality
NA_Sales is highly correlated with EU_Sales and 1 other fieldsHigh correlation
EU_Sales is highly correlated with NA_Sales and 1 other fieldsHigh correlation
Critic_Score is highly correlated with User_ScoreHigh correlation
Critic_Count is highly correlated with User_CountHigh correlation
User_Score is highly correlated with Critic_ScoreHigh correlation
User_Count is highly correlated with Critic_CountHigh correlation
Global_Sales is highly correlated with NA_Sales and 1 other fieldsHigh correlation
NA_Sales is highly correlated with EU_Sales and 1 other fieldsHigh correlation
EU_Sales is highly correlated with NA_Sales and 1 other fieldsHigh correlation
JP_Sales is highly correlated with Global_SalesHigh correlation
Global_Sales is highly correlated with NA_Sales and 2 other fieldsHigh correlation
NA_Sales is highly correlated with EU_Sales and 1 other fieldsHigh correlation
EU_Sales is highly correlated with NA_Sales and 1 other fieldsHigh correlation
Global_Sales is highly correlated with NA_Sales and 1 other fieldsHigh correlation
Platform is highly correlated with Year_of_ReleaseHigh correlation
Year_of_Release is highly correlated with PlatformHigh correlation
Genre is highly correlated with RatingHigh correlation
NA_Sales is highly correlated with EU_Sales and 2 other fieldsHigh correlation
EU_Sales is highly correlated with NA_Sales and 2 other fieldsHigh correlation
JP_Sales is highly correlated with NA_Sales and 2 other fieldsHigh correlation
Critic_Score is highly correlated with User_ScoreHigh correlation
Critic_Count is highly correlated with User_CountHigh correlation
User_Score is highly correlated with Critic_ScoreHigh correlation
User_Count is highly correlated with Critic_CountHigh correlation
Rating is highly correlated with GenreHigh correlation
Global_Sales is highly correlated with NA_Sales and 2 other fieldsHigh correlation
Year_of_Release has 232 (1.6%) missing values Missing
Critic_Score has 7359 (50.5%) missing values Missing
Critic_Count has 7359 (50.5%) missing values Missing
User_Score has 5816 (39.9%) missing values Missing
User_Count has 7780 (53.4%) missing values Missing
Developer has 5747 (39.4%) missing values Missing
Rating has 5872 (40.3%) missing values Missing
Name is uniformly distributed Uniform
NA_Sales has 3861 (26.5%) zeros Zeros
EU_Sales has 4864 (33.4%) zeros Zeros
JP_Sales has 9019 (61.9%) zeros Zeros
User_Score has 1965 (13.5%) zeros Zeros

Reproduction

Analysis started2022-04-25 04:08:00.647214
Analysis finished2022-04-25 04:08:13.051212
Duration12.4 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct10327
Distinct (%)70.9%
Missing2
Missing (%)< 0.1%
Memory size114.0 KiB
Need for Speed: Most Wanted
 
11
Madden NFL 07
 
9
LEGO Marvel Super Heroes
 
9
Ratatouille
 
9
FIFA 14
 
9
Other values (10322)
14527 

Length

Max length124
Median length22
Mean length23.81357211
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7952 ?
Unique (%)54.6%

Sample

1st rowWii Sports
2nd rowSuper Mario Bros.
3rd rowMario Kart Wii
4th rowWii Sports Resort
5th rowPokemon Red/Pokemon Blue

Common Values

ValueCountFrequency (%)
Need for Speed: Most Wanted11
 
0.1%
Madden NFL 079
 
0.1%
LEGO Marvel Super Heroes9
 
0.1%
Ratatouille9
 
0.1%
FIFA 149
 
0.1%
LEGO The Hobbit8
 
0.1%
Monopoly8
 
0.1%
FIFA 158
 
0.1%
Cars8
 
0.1%
FIFA Soccer 138
 
0.1%
Other values (10317)14487
99.4%

Length

2022-04-25T09:38:13.151219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the2403
 
4.2%
of1493
 
2.6%
21058
 
1.8%
no656
 
1.1%
649
 
1.1%
3474
 
0.8%
world359
 
0.6%
pro273
 
0.5%
game272
 
0.5%
ii262
 
0.5%
Other values (8646)49640
86.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Platform
Categorical

HIGH CORRELATION

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size114.0 KiB
PS2
1935 
DS
1783 
PS3
1209 
Wii
1138 
X360
1135 
Other values (26)
7376 

Length

Max length4
Median length3
Mean length2.781970362
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowWii
2nd rowNES
3rd rowWii
4th rowWii
5th rowGB

Common Values

ValueCountFrequency (%)
PS21935
13.3%
DS1783
12.2%
PS31209
8.3%
Wii1138
 
7.8%
X3601135
 
7.8%
PS1058
 
7.3%
PSP1049
 
7.2%
PC874
 
6.0%
GBA712
 
4.9%
XB693
 
4.8%
Other values (21)2990
20.5%

Length

2022-04-25T09:38:13.259509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ps21935
13.3%
ds1783
12.2%
ps31209
8.3%
wii1138
 
7.8%
x3601135
 
7.8%
ps1058
 
7.3%
psp1049
 
7.2%
pc874
 
6.0%
gba712
 
4.9%
xb693
 
4.8%
Other values (21)2990
20.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Year_of_Release
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct39
Distinct (%)0.3%
Missing232
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean2006.437117
Minimum1980
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.0 KiB
2022-04-25T09:38:13.355229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1980
5-th percentile1996
Q12003
median2007
Q32010
95-th percentile2015
Maximum2020
Range40
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.955664119
Coefficient of variation (CV)0.002968278482
Kurtosis1.879039188
Mean2006.437117
Median Absolute Deviation (MAD)4
Skewness-1.009238323
Sum28780334
Variance35.4699351
MonotonicityNot monotonic
2022-04-25T09:38:13.447464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
20091248
 
8.6%
20081213
 
8.3%
20101092
 
7.5%
20071067
 
7.3%
2011955
 
6.6%
2006886
 
6.1%
2005795
 
5.5%
2002718
 
4.9%
2003675
 
4.6%
2004664
 
4.6%
Other values (29)5031
34.5%
ValueCountFrequency (%)
19809
 
0.1%
198145
0.3%
198236
0.2%
198317
 
0.1%
198414
 
0.1%
198514
 
0.1%
198621
0.1%
198714
 
0.1%
198815
 
0.1%
198917
 
0.1%
ValueCountFrequency (%)
20201
 
< 0.1%
20173
 
< 0.1%
2016435
 
3.0%
2015536
3.7%
2014498
 
3.4%
2013474
 
3.3%
2012591
4.1%
2011955
6.6%
20101092
7.5%
20091248
8.6%

Genre
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)0.1%
Missing2
Missing (%)< 0.1%
Memory size114.0 KiB
Action
2974 
Sports
2078 
Misc
1503 
Role-Playing
1300 
Shooter
1171 
Other values (7)
5548 

Length

Max length12
Median length6
Mean length7.135583917
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSports
2nd rowPlatform
3rd rowRacing
4th rowSports
5th rowRole-Playing

Common Values

ValueCountFrequency (%)
Action2974
20.4%
Sports2078
14.3%
Misc1503
10.3%
Role-Playing1300
8.9%
Shooter1171
 
8.0%
Adventure1127
 
7.7%
Racing1084
 
7.4%
Platform786
 
5.4%
Simulation753
 
5.2%
Fighting728
 
5.0%
Other values (2)1070
 
7.3%

Length

2022-04-25T09:38:13.543429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
action2974
20.4%
sports2078
14.3%
misc1503
10.3%
role-playing1300
8.9%
shooter1171
 
8.0%
adventure1127
 
7.7%
racing1084
 
7.4%
platform786
 
5.4%
simulation753
 
5.2%
fighting728
 
5.0%
Other values (2)1070
 
7.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Publisher
Categorical

HIGH CARDINALITY

Distinct552
Distinct (%)3.8%
Missing49
Missing (%)0.3%
Memory size114.0 KiB
Electronic Arts
1267 
Activision
 
881
Ubisoft
 
803
Namco Bandai Games
 
778
Konami Digital Entertainment
 
709
Other values (547)
10089 

Length

Max length38
Median length11
Mean length13.64438632
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique176 ?
Unique (%)1.2%

Sample

1st rowNintendo
2nd rowNintendo
3rd rowNintendo
4th rowNintendo
5th rowNintendo

Common Values

ValueCountFrequency (%)
Electronic Arts1267
 
8.7%
Activision881
 
6.0%
Ubisoft803
 
5.5%
Namco Bandai Games778
 
5.3%
Konami Digital Entertainment709
 
4.9%
Nintendo673
 
4.6%
THQ638
 
4.4%
Sony Computer Entertainment628
 
4.3%
Sega542
 
3.7%
Take-Two Interactive377
 
2.6%
Other values (542)7231
49.6%

Length

2022-04-25T09:38:13.639229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
entertainment2142
 
8.1%
games1671
 
6.3%
interactive1446
 
5.5%
arts1270
 
4.8%
electronic1269
 
4.8%
activision904
 
3.4%
ubisoft816
 
3.1%
digital798
 
3.0%
namco778
 
2.9%
bandai778
 
2.9%
Other values (617)14593
55.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NA_Sales
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct402
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2955769759
Minimum0
Maximum41.36
Zeros3861
Zeros (%)26.5%
Negative0
Negative (%)0.0%
Memory size114.0 KiB
2022-04-25T09:38:13.739229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.1
Q30.28
95-th percentile1.19
Maximum41.36
Range41.36
Interquartile range (IQR)0.28

Descriptive statistics

Standard deviation0.8664905241
Coefficient of variation (CV)2.931522395
Kurtosis575.1870412
Mean0.2955769759
Median Absolute Deviation (MAD)0.1
Skewness17.71213844
Sum4308.33
Variance0.7508058283
MonotonicityNot monotonic
2022-04-25T09:38:13.835220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03861
26.5%
0.02542
 
3.7%
0.01508
 
3.5%
0.03446
 
3.1%
0.04376
 
2.6%
0.08368
 
2.5%
0.09348
 
2.4%
0.1336
 
2.3%
0.05329
 
2.3%
0.13308
 
2.1%
Other values (392)7154
49.1%
ValueCountFrequency (%)
03861
26.5%
0.01508
 
3.5%
0.02542
 
3.7%
0.03446
 
3.1%
0.04376
 
2.6%
0.05329
 
2.3%
0.06173
 
1.2%
0.07281
 
1.9%
0.08368
 
2.5%
0.09348
 
2.4%
ValueCountFrequency (%)
41.361
< 0.1%
29.081
< 0.1%
26.931
< 0.1%
23.21
< 0.1%
15.681
< 0.1%
15.611
< 0.1%
151
< 0.1%
14.441
< 0.1%
13.961
< 0.1%
12.781
< 0.1%

EU_Sales
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct307
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1639571899
Minimum0
Maximum28.96
Zeros4864
Zeros (%)33.4%
Negative0
Negative (%)0.0%
Memory size114.0 KiB
2022-04-25T09:38:13.931229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.03
Q30.13
95-th percentile0.69
Maximum28.96
Range28.96
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.5363536856
Coefficient of variation (CV)3.271303235
Kurtosis669.1637779
Mean0.1639571899
Median Absolute Deviation (MAD)0.03
Skewness17.76300594
Sum2389.84
Variance0.2876752761
MonotonicityNot monotonic
2022-04-25T09:38:14.027230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04864
33.4%
0.011275
 
8.7%
0.02990
 
6.8%
0.03648
 
4.4%
0.04638
 
4.4%
0.05430
 
3.0%
0.06362
 
2.5%
0.07326
 
2.2%
0.08299
 
2.1%
0.09263
 
1.8%
Other values (297)4481
30.7%
ValueCountFrequency (%)
04864
33.4%
0.011275
 
8.7%
0.02990
 
6.8%
0.03648
 
4.4%
0.04638
 
4.4%
0.05430
 
3.0%
0.06362
 
2.5%
0.07326
 
2.2%
0.08299
 
2.1%
0.09263
 
1.8%
ValueCountFrequency (%)
28.961
< 0.1%
12.761
< 0.1%
10.951
< 0.1%
10.931
< 0.1%
9.21
< 0.1%
9.181
< 0.1%
9.141
< 0.1%
9.091
< 0.1%
8.891
< 0.1%
8.491
< 0.1%

JP_Sales
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct244
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0856586169
Minimum0
Maximum10.22
Zeros9019
Zeros (%)61.9%
Negative0
Negative (%)0.0%
Memory size114.0 KiB
2022-04-25T09:38:14.127420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.03
95-th percentile0.41
Maximum10.22
Range10.22
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.3296458183
Coefficient of variation (CV)3.848367277
Kurtosis170.2991701
Mean0.0856586169
Median Absolute Deviation (MAD)0
Skewness10.49877206
Sum1248.56
Variance0.1086663655
MonotonicityNot monotonic
2022-04-25T09:38:14.227418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09019
61.9%
0.02722
 
5.0%
0.01684
 
4.7%
0.03523
 
3.6%
0.04380
 
2.6%
0.05304
 
2.1%
0.06176
 
1.2%
0.1153
 
1.0%
0.09140
 
1.0%
0.11138
 
0.9%
Other values (234)2337
 
16.0%
ValueCountFrequency (%)
09019
61.9%
0.01684
 
4.7%
0.02722
 
5.0%
0.03523
 
3.6%
0.04380
 
2.6%
0.05304
 
2.1%
0.06176
 
1.2%
0.0786
 
0.6%
0.0893
 
0.6%
0.09140
 
1.0%
ValueCountFrequency (%)
10.221
< 0.1%
7.21
< 0.1%
6.811
< 0.1%
6.51
< 0.1%
6.041
< 0.1%
5.651
< 0.1%
5.381
< 0.1%
5.331
< 0.1%
5.321
< 0.1%
4.871
< 0.1%

Critic_Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct80
Distinct (%)1.1%
Missing7359
Missing (%)50.5%
Infinite0
Infinite (%)0.0%
Mean69.67604268
Minimum13
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.0 KiB
2022-04-25T09:38:14.331229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile44
Q161
median71
Q380
95-th percentile89
Maximum98
Range85
Interquartile range (IQR)19

Descriptive statistics

Standard deviation13.77339065
Coefficient of variation (CV)0.1976775677
Kurtosis0.1621482683
Mean69.67604268
Median Absolute Deviation (MAD)9
Skewness-0.6276894676
Sum502852
Variance189.7062899
MonotonicityNot monotonic
2022-04-25T09:38:14.423467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71235
 
1.6%
76221
 
1.5%
70220
 
1.5%
75217
 
1.5%
78216
 
1.5%
80215
 
1.5%
73212
 
1.5%
77212
 
1.5%
81211
 
1.4%
74204
 
1.4%
Other values (70)5054
34.7%
(Missing)7359
50.5%
ValueCountFrequency (%)
131
 
< 0.1%
171
 
< 0.1%
195
< 0.1%
201
 
< 0.1%
233
 
< 0.1%
242
 
< 0.1%
256
< 0.1%
2611
0.1%
276
< 0.1%
287
< 0.1%
ValueCountFrequency (%)
984
 
< 0.1%
9711
 
0.1%
9618
 
0.1%
9516
 
0.1%
9437
 
0.3%
9347
0.3%
9253
0.4%
9167
0.5%
9073
0.5%
89100
0.7%

Critic_Count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct106
Distinct (%)1.5%
Missing7359
Missing (%)50.5%
Infinite0
Infinite (%)0.0%
Mean27.31204101
Minimum3
Maximum113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.0 KiB
2022-04-25T09:38:14.519229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q112
median22
Q338
95-th percentile67
Maximum113
Range110
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.46419649
Coefficient of variation (CV)0.7126599027
Kurtosis0.8069792626
Mean27.31204101
Median Absolute Deviation (MAD)12
Skewness1.091984852
Sum197111
Variance378.8549451
MonotonicityNot monotonic
2022-04-25T09:38:14.611426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4259
 
1.8%
5222
 
1.5%
9214
 
1.5%
11213
 
1.5%
7207
 
1.4%
8203
 
1.4%
17203
 
1.4%
6200
 
1.4%
16197
 
1.4%
13194
 
1.3%
Other values (96)5105
35.0%
(Missing)7359
50.5%
ValueCountFrequency (%)
31
 
< 0.1%
4259
1.8%
5222
1.5%
6200
1.4%
7207
1.4%
8203
1.4%
9214
1.5%
10184
1.3%
11213
1.5%
12188
1.3%
ValueCountFrequency (%)
1131
 
< 0.1%
1071
 
< 0.1%
1061
 
< 0.1%
1051
 
< 0.1%
1041
 
< 0.1%
1031
 
< 0.1%
1021
 
< 0.1%
1012
< 0.1%
1003
< 0.1%
991
 
< 0.1%

User_Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct95
Distinct (%)1.1%
Missing5816
Missing (%)39.9%
Infinite0
Infinite (%)0.0%
Mean5.549006849
Minimum0
Maximum9.7
Zeros1965
Zeros (%)13.5%
Negative0
Negative (%)0.0%
Memory size114.0 KiB
2022-04-25T09:38:14.711229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.6
median7
Q38
95-th percentile8.8
Maximum9.7
Range9.7
Interquartile range (IQR)4.4

Descriptive statistics

Standard deviation3.25543152
Coefficient of variation (CV)0.58666922
Kurtosis-0.850241188
Mean5.549006849
Median Absolute Deviation (MAD)1.3
Skewness-0.8719537997
Sum48609.3
Variance10.59783438
MonotonicityNot monotonic
2022-04-25T09:38:14.811234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01965
 
13.5%
7.8284
 
1.9%
8.2254
 
1.7%
8252
 
1.7%
8.5235
 
1.6%
8.3232
 
1.6%
7.5230
 
1.6%
7.9229
 
1.6%
8.1224
 
1.5%
7.3213
 
1.5%
Other values (85)4642
31.8%
(Missing)5816
39.9%
ValueCountFrequency (%)
01965
13.5%
0.21
 
< 0.1%
0.32
 
< 0.1%
0.52
 
< 0.1%
0.62
 
< 0.1%
0.72
 
< 0.1%
0.91
 
< 0.1%
12
 
< 0.1%
1.12
 
< 0.1%
1.23
 
< 0.1%
ValueCountFrequency (%)
9.71
 
< 0.1%
9.62
 
< 0.1%
9.56
 
< 0.1%
9.410
 
0.1%
9.328
 
0.2%
9.238
 
0.3%
9.185
0.6%
9109
0.7%
8.9142
1.0%
8.8167
1.1%

User_Count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct868
Distinct (%)12.8%
Missing7780
Missing (%)53.4%
Infinite0
Infinite (%)0.0%
Mean172.5130959
Minimum4
Maximum10665
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.0 KiB
2022-04-25T09:38:15.067424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q111
median27
Q389
95-th percentile779.5
Maximum10665
Range10661
Interquartile range (IQR)78

Descriptive statistics

Standard deviation576.6097163
Coefficient of variation (CV)3.342411271
Kurtosis100.4416536
Mean172.5130959
Median Absolute Deviation (MAD)20
Skewness8.534899458
Sum1172399
Variance332478.7649
MonotonicityNot monotonic
2022-04-25T09:38:15.167491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4285
 
2.0%
6281
 
1.9%
5266
 
1.8%
8244
 
1.7%
7223
 
1.5%
9209
 
1.4%
10157
 
1.1%
11145
 
1.0%
13138
 
0.9%
12136
 
0.9%
Other values (858)4712
32.3%
(Missing)7780
53.4%
ValueCountFrequency (%)
4285
2.0%
5266
1.8%
6281
1.9%
7223
1.5%
8244
1.7%
9209
1.4%
10157
1.1%
11145
1.0%
12136
0.9%
13138
0.9%
ValueCountFrequency (%)
106651
< 0.1%
101791
< 0.1%
96291
< 0.1%
90731
< 0.1%
87131
< 0.1%
86651
< 0.1%
80031
< 0.1%
75121
< 0.1%
73221
< 0.1%
70641
< 0.1%

Developer
Categorical

HIGH CARDINALITY
MISSING

Distinct1577
Distinct (%)17.9%
Missing5747
Missing (%)39.4%
Memory size114.0 KiB
Ubisoft
 
171
EA Sports
 
167
EA Canada
 
158
Konami
 
137
Capcom
 
123
Other values (1572)
8073 

Length

Max length80
Median length13
Mean length13.40593499
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique615 ?
Unique (%)7.0%

Sample

1st rowNintendo
2nd rowNintendo
3rd rowNintendo
4th rowNintendo
5th rowNintendo

Common Values

ValueCountFrequency (%)
Ubisoft171
 
1.2%
EA Sports167
 
1.1%
EA Canada158
 
1.1%
Konami137
 
0.9%
Capcom123
 
0.8%
EA Tiburon102
 
0.7%
Ubisoft Montreal94
 
0.6%
Electronic Arts94
 
0.6%
Visual Concepts89
 
0.6%
Traveller's Tales75
 
0.5%
Other values (1567)7619
52.3%
(Missing)5747
39.4%

Length

2022-04-25T09:38:15.283229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
games936
 
5.4%
studios691
 
4.0%
ea608
 
3.5%
entertainment561
 
3.2%
software436
 
2.5%
ubisoft404
 
2.3%
interactive297
 
1.7%
sports230
 
1.3%
canada168
 
1.0%
inc161
 
0.9%
Other values (1545)12878
74.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Rating
Categorical

HIGH CORRELATION
MISSING

Distinct8
Distinct (%)0.1%
Missing5872
Missing (%)40.3%
Memory size114.0 KiB
E
3460 
T
2580 
M
1410 
E10+
1240 
EC
 
7
Other values (3)
 
7

Length

Max length4
Median length1
Mean length1.429342831
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowE
2nd rowE
3rd rowE
4th rowE
5th rowE

Common Values

ValueCountFrequency (%)
E3460
23.7%
T2580
17.7%
M1410
 
9.7%
E10+1240
 
8.5%
EC7
 
< 0.1%
RP3
 
< 0.1%
K-A3
 
< 0.1%
AO1
 
< 0.1%
(Missing)5872
40.3%

Length

2022-04-25T09:38:15.379229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-25T09:38:15.435229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
e3460
39.8%
t2580
29.6%
m1410
16.2%
e101240
 
14.2%
ec7
 
0.1%
k-a3
 
< 0.1%
rp3
 
< 0.1%
ao1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Global_Sales
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct626
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5989715971
Minimum0.01
Maximum82.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.0 KiB
2022-04-25T09:38:15.523229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.02
Q10.05
median0.22
Q30.55
95-th percentile2.22
Maximum82.53
Range82.52
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation1.647698962
Coefficient of variation (CV)2.750879958
Kurtosis536.8290458
Mean0.5989715971
Median Absolute Deviation (MAD)0.19
Skewness16.41123507
Sum8730.61
Variance2.714911869
MonotonicityDecreasing
2022-04-25T09:38:15.615229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.021087
 
7.5%
0.03852
 
5.8%
0.04651
 
4.5%
0.01638
 
4.4%
0.05631
 
4.3%
0.1415
 
2.8%
0.11397
 
2.7%
0.09349
 
2.4%
0.14324
 
2.2%
0.15283
 
1.9%
Other values (616)8949
61.4%
ValueCountFrequency (%)
0.01638
4.4%
0.021087
7.5%
0.03852
5.8%
0.04651
4.5%
0.05631
4.3%
0.06216
 
1.5%
0.09349
 
2.4%
0.1415
 
2.8%
0.11397
 
2.7%
0.1331
 
0.2%
ValueCountFrequency (%)
82.531
< 0.1%
40.241
< 0.1%
35.521
< 0.1%
32.771
< 0.1%
31.371
< 0.1%
30.261
< 0.1%
29.81
< 0.1%
28.921
< 0.1%
28.321
< 0.1%
28.311
< 0.1%

Interactions

2022-04-25T09:38:11.459212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:04.771201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:05.847223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:06.691229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:07.483213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:08.263205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:09.115218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:09.891212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:10.679222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:11.551224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:04.951216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:05.943221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:06.787221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:07.579211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:08.351212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:09.207211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:09.987233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:10.767215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:11.635211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:05.083230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:06.027215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:06.875241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:07.663213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:08.431213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:09.291214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:10.071230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:10.851216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:11.723207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:05.215221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:06.115535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:06.967433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:07.755230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:08.515219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:09.379211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:10.159212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:10.947211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:11.811210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:05.335228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:06.195217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:07.051214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:07.839221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:08.603563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:09.463210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:10.251210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:11.031211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:12.027202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:05.447214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:06.351229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:07.135223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:07.923213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:08.683220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:09.551211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:10.335213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:11.115217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:12.111216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:05.555213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:06.439199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:07.223212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:08.007217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:08.863230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:09.635224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:10.423229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:11.203215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:12.195213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:05.655223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:06.519222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:07.307216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:08.091212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:08.947214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:09.723219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:10.507215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:11.287214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:12.283219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:05.751426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:06.603210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:07.399420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:08.179216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:09.031212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:09.807213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:10.595211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-25T09:38:11.375205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-04-25T09:38:15.695229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-25T09:38:15.811526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-25T09:38:15.923229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-25T09:38:16.027230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-25T09:38:16.111209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-25T09:38:12.419229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-25T09:38:12.647205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-04-25T09:38:12.831229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-04-25T09:38:12.955429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

NamePlatformYear_of_ReleaseGenrePublisherNA_SalesEU_SalesJP_SalesCritic_ScoreCritic_CountUser_ScoreUser_CountDeveloperRatingGlobal_Sales
0Wii SportsWii2006.0SportsNintendo41.3628.963.7776.051.08.0322.0NintendoE82.53
1Super Mario Bros.NES1985.0PlatformNintendo29.083.586.81NaNNaNNaNNaNNaNNaN40.24
2Mario Kart WiiWii2008.0RacingNintendo15.6812.763.7982.073.08.3709.0NintendoE35.52
3Wii Sports ResortWii2009.0SportsNintendo15.6110.933.2880.073.08.0192.0NintendoE32.77
4Pokemon Red/Pokemon BlueGB1996.0Role-PlayingNintendo11.278.8910.22NaNNaNNaNNaNNaNNaN31.37
5TetrisGB1989.0PuzzleNintendo23.202.264.22NaNNaNNaNNaNNaNNaN30.26
6New Super Mario Bros.DS2006.0PlatformNintendo11.289.146.5089.065.08.5431.0NintendoE29.80
7Wii PlayWii2006.0MiscNintendo13.969.182.9358.041.06.6129.0NintendoE28.92
8New Super Mario Bros. WiiWii2009.0PlatformNintendo14.446.944.7087.080.08.4594.0NintendoE28.32
9Duck HuntNES1984.0ShooterNintendo26.930.630.28NaNNaNNaNNaNNaNNaN28.31

Last rows

NamePlatformYear_of_ReleaseGenrePublisherNA_SalesEU_SalesJP_SalesCritic_ScoreCritic_CountUser_ScoreUser_CountDeveloperRatingGlobal_Sales
1456615 DaysPC2009.0AdventureDTP Entertainment0.000.010.0063.06.05.88.0DTP EntertainmentNaN0.01
14567Men in Black II: Alien EscapeGC2003.0ShooterInfogrames0.010.000.00NaNNaN0.0NaNAtariT0.01
14568Aiyoku no EustiaPSV2014.0Miscdramatic create0.000.000.01NaNNaNNaNNaNNaNNaN0.01
14569Woody Woodpecker in Crazy Castle 5GBA2002.0PlatformKemco0.010.000.00NaNNaNNaNNaNNaNNaN0.01
14570SCORE International Baja 1000: The Official GamePS22008.0RacingActivision0.000.000.00NaNNaNNaNNaNNaNNaN0.01
14571Samurai Warriors: Sanada MaruPS32016.0ActionTecmo Koei0.000.000.01NaNNaNNaNNaNNaNNaN0.01
14572LMA Manager 2007X3602006.0SportsCodemasters0.000.010.00NaNNaNNaNNaNNaNNaN0.01
14573Haitaka no PsychedelicaPSV2016.0AdventureIdea Factory0.000.000.01NaNNaNNaNNaNNaNNaN0.01
14574Spirits & SpellsGBA2003.0PlatformWanadoo0.010.000.00NaNNaNNaNNaNNaNNaN0.01
14575Winning Post 8 2016PSV2016.0SimulationTecmo Koei0.000.000.01NaNNaNNaNNaNNaNNaN0.01